R: Ecological Niche Modeling using Presence-Absence Data

# Ecological Niche Modeling using Presence-Absence Data

## Documentation for package ‘enmpa’ version 0.1.5

- DESCRIPTION file.
- Package NEWS.

## Help Pages

aux_string_comb | Get GLM formulas according to defined response types
aux_var_comb | Get GLM formulas according to defined response types
calibration_glm | GLM calibration with presence-absence data
cal_res | Example of results obtained from GLM calibration using enmpa
enmpa | enmpa: Ecological Niche Modeling using Presence-Absence Data
enm_data | Example data used to run model calibration exercises
evaluation_stats | Summary of evaluation statistics for candidate models
fit_glms | Fitting selected GLMs models
fit_selected | Fitting selected GLMs models
get_formulas | Get GLM formulas according to defined response types
get_formulas_main | Get GLM formulas according to defined response types
independent_eval01 | Evaluate final models using independent data
independent_eval1 | Evaluate final models using independent data
kfold_partition | K-fold data partitioning
model_selection | Selection of best candidate models considering various criteria
model_validation | Model validation options
niche_signal | Niche Signal detection using one or multiple variables
niche_signal_permanova | Niche Signal detection using one or multiple variables
niche_signal_univariate | Niche Signal detection using one or multiple variables
optimize_metrics | Find threshold values to produce three optimal metrics
plot_importance | Plot variable importance
plot_niche_signal | Plot Niche Signal results
plot_niche_signal_permanova | Plot Niche Signal results
plot_niche_signal_univariate | Plot Niche Signal results
predict_glm | Extension of glm predict to generate predictions of different types
predict_selected | Predictions for the models selected after calibration
proc_enm | Partial ROC calculation
response_curve | Variable response curves for GLMs
sel_fit | Example of selected models fitted
test | Example data used to test models
var_importance | Variable importance for GLMs